{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from queue import PriorityQueue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.array([[-2.1, 4.1], [1.0, -2.7], [2.2, -0.2], [-3.1, 1.1]])\n",
    "alpha = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getFitness(c):\n",
    "    fitness = []\n",
    "    for i in range(4):\n",
    "        fitness.append(1 / (c[i][0] * c[i][0] + c[i][1] * c[i][1] + 1))\n",
    "\n",
    "    return np.array(fitness)\n",
    "\n",
    "def getMeanAndDiv(c, k = 4):\n",
    "    mu = np.array([0.0, 0.0])\n",
    "    ga = np.array([0.0, 0.0])\n",
    "    \n",
    "    for i in range(k):\n",
    "        mu[0] += c[i][0]\n",
    "        mu[1] += c[i][1]\n",
    "    mu[0] = mu[0] / k\n",
    "    mu[1] = mu[1] / k\n",
    "\n",
    "    for i in range(k):\n",
    "        for j in range(2):\n",
    "            ga[j] = ga[j] + (c[i][j] - mu[j]) * (c[i][j] - mu[j])\n",
    "\n",
    "    for i in range(2):\n",
    "        ga[i] = np.math.sqrt(ga[i] / k)\n",
    "\n",
    "    return mu, ga\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_fig():\n",
    "    x = np.arange(-5, 5, 0.2)\n",
    "    y = np.arange(-5, 5, 0.2)\n",
    "    \n",
    "    fig = plt.figure()  \n",
    "    ax3 = plt.axes(projection='3d')\n",
    "\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "    Z = 1 / (np.power(X, 2)+np.power(Y, 2) + 1)\n",
    "    ax3.plot_surface(X, Y, Z, cmap='rainbow')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_fig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def renewGaussDistribution(c, mu_, ga_, alpha):\n",
    "    mu = mu_.copy()\n",
    "    ga = ga_.copy()\n",
    "\n",
    "    fitness:np.array = getFitness(c)\n",
    "    q = PriorityQueue()\n",
    "    for i in range(4):\n",
    "        q.put_nowait((fitness[i], i))\n",
    "    last, last_idx = q.get()\n",
    "    _ = q.get()\n",
    "    second, second_idx = q.get()\n",
    "    first, first_idx = q.get()\n",
    "\n",
    "    c_tmp = np.array([c[first_idx], c[second_idx]])\n",
    "\n",
    "    mu_tmp, ga_tmp = getMeanAndDiv(c_tmp, 2)\n",
    "\n",
    "    for i in range(2):\n",
    "        mu[i] = (1 - alpha) * mu[i] + alpha * (c[first_idx][i] + c[second_idx][i] - c[last_idx][i])\n",
    "        ga[i] = (1 - alpha) * ga[i] + alpha * ga_tmp[i]\n",
    "    return mu, ga\n",
    "\n",
    "def getNewSamples(mu, ga):\n",
    "    x1 = np.random.normal(mu[0], ga[0], size=4)\n",
    "    x2 = np.random.normal(mu[1], ga[1], size=4)\n",
    "    c_new = np.array([[x1[i], x2[i]] for i in range(4)])\n",
    "    return c_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EDA(c_first, step = 100, alpha = 0.5):\n",
    "    x1_best = []\n",
    "    x2_best = []\n",
    "    fitness_best = []\n",
    "    c = c_first\n",
    "    mu, ga = getMeanAndDiv(c)\n",
    "    for i in range(step):\n",
    "        fitness = getFitness(c)\n",
    "        # print(fitness)\n",
    "        maxFitness = np.max(fitness)\n",
    "        maxFitness_idx = np.argmax(fitness)\n",
    "        x1_best.append(c[maxFitness_idx][0])\n",
    "        x2_best.append(c[maxFitness_idx][1])\n",
    "        fitness_best.append(maxFitness)\n",
    "        mu, ga = renewGaussDistribution(c, mu, ga, alpha)\n",
    "        c = getNewSamples(mu, ga)\n",
    "    return x1_best, x2_best, fitness_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1, x2, fitness = EDA(c)\n",
    "\n",
    "plt.plot(x1)\n",
    "plt.show()\n",
    "plt.plot(x2)\n",
    "plt.show()\n",
    "plt.plot(fitness)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "79c38198e3a853eeea51b70a0ae21c719ed1444592bdd4a45c4de9d24fbfec7c"
  },
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
